Credit Score

1 Introduction

Create a model capable of estimating the creditworthiness of customers, in order to help the dedicated team understand whether or not to accept a credit card application.

1.1 Features

• ID: customer identification number
• CODE_GENDER: gender of the customer
• FLAGOWNCAR: indicator of car ownership
• FLAGOWNREALTY: indicator of house ownership
• CNT_CHILDREN: number of children
• AMTINCOMETOTAL: annual income
• NAMEINCOMETYPE: type of income
• NAMEEDUCATIONTYPE: level of education
• NAMEFAMILYSTATUS: marital status
• NAMEHOUSINGTYPE: type of dwelling
• DAYS_BIRTH: number of days since birth
• DAYS_EMPLOYED: number of days since employment (if positive, indicates the number of days since being unemployed)
• FLAG_MOBIL: indicator for the presence of a mobile phone number
• FLAGWORKPHONE: indicator of the presence of a work phone number
• FLAG_PHONE: indicator of the presence of a telephone number
• FLAG_EMAIL: indicator for the presence of an email address
• OCCUPATION_TYPE: type of occupation
• CNTFAMMEMBERS: number of family members
• TARGET: variable which is worth 1 if the customer has a high creditworthiness (constant payment of installments), 0 otherwise.

If a customer is denied a credit card, the team must be able to give a reason. This means that your model must provide indications that are easy to interpret.

It is a binary classification which needs a good explainability.

2 Import

2.1 R

library(tidyverse, verbose = FALSE)
library(ggplot2)
library(plotly)
library(reticulate)
library(gt)
library(scales)

2.2 Python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pickle

from datetime import date
from dateutil.relativedelta import relativedelta
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler, LabelBinarizer
from sklearn.pipeline import Pipeline
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

2.3 Config class

class Config():
  
  def __init__(self):
    """
    Initialization calss.
    """
    
    self.path="/Users/simonebrazzi/R/blog/posts/credit_score/"
    self.file="/Users/simonebrazzi/R/blog/posts/credit_score/credit_scoring.csv"
    self.random_state=42
    self.col_bi = ['code_gender', 'flag_own_car', 'flag_own_realty', 'flag_mobil', 'flag_work_phone', 'flag_phone', 'flag_email']
    self.col_ohe = ["name_income_type", "name_education_type", "name_family_status", "name_housing_type", "occupation_type"]
    self.col_ss = ['cnt_children', 'amt_income_total', 'cnt_fam_members']
    self.features = self.col_bi + self.col_ohe + self.col_ss

  
config = Config()

3 Dataset

Now we can import the dataset. Having all columns UPPERCASE, I will set all of them as lowercase. Also, I will assign target as the y variable and all the remaining columns as x or features.

df = pd.read_csv(config.file)
df.columns = df.columns.str.lower()
df["id"] = df.id.astype(int)

4 EDA

4.1 Target

It is always important to perform an EDA. In this case I am expecting to have less cases of high creditworthiness than low leveraging personal knowledge. Xonsidering it is a binary classification, it is important to be sure if data are balanced to make a better model selection.

The variable:

• 0 has 308,705.
• 1 has 29,722.

df <- py$df

df_g <- df %>% 
  rename(label = target) %>% 
  mutate(label = as.factor(label)) %>% 
  summarize(
    freq_abs = n(),
    freq_rel = n() / nrow(df),
    .by = label
  )
df_g %>% 
  gt() %>% 
  fmt_auto() %>% 
  cols_width(
    label ~ pct(20),
    freq_abs ~ pct(35),
    freq_rel ~ pct(35)
    ) %>% 
  cols_align(
    align = "center",
    columns = c(freq_abs, freq_rel)
  ) %>% 
  tab_header(
    title = "Label frequency",
    subtitle = "Absolute and relative frequencies"
  ) %>% 
  cols_label(
    label = "Label",
    freq_abs = "Absolute frequency",
    freq_rel = "Relative frequency"
  ) %>% 
  tab_options(
    table.width = pct(100)
    )

Table 1: Absolute and relative frequencies

Label frequency
Absolute and relative frequencies
Label	Absolute frequency	Relative frequency
0	308,705	0.912
1	29,722	0.088

g <- ggplot(df_g) +
  geom_bar(aes(x = label, y = freq_abs, fill = label), stat = "identity") +
  ggtitle("Absolute frequency barchart") +
  xlab("Label") +
  ylab("Absolute frequency") +
  labs(fill = "Label") +
  scale_y_continuous(breaks = scales::breaks_pretty(n = 5)) +
  theme_minimal()
ggplotly(g)

Figure 1: Absolute frequency barchart

Remember: 0. stands for low creditworthiness, 1 for high. Our dataset is strongly unbalanced towards the 0 label. So, my first idea was right.

4.2 Features

Lets check the NA values in each feature.

df_na <- df %>%
  summarize(across(everything(), ~ sum(is.na(.)))) %>%
  pivot_longer(
    cols = everything(),
    names_to = "columns",
    values_to = "value"
  )
df_na %>% 
  gt() %>% 
  tab_options(
    table.width = pct(100)
    ) %>% 
  cols_align(align = "center") %>% 
  cols_label(
    columns = "Columns",
    value = "NA Value"
  ) %>% 
  tab_header(
    title = "NA values",
    subtitle = "Number of NA value for each feature"
  ) %>% 
  fmt_number(
    columns = c("value"),
    decimals = 2,
    drop_trailing_zeros = TRUE,
    #drop_trailing_dec_mark = FALSE
  ) 

Table 2: Number of NA values for each feature

NA values
Number of NA value for each feature
Columns	NA Value
id	0
code_gender	0
flag_own_car	0
flag_own_realty	0
cnt_children	0
amt_income_total	0
name_income_type	0
name_education_type	0
name_family_status	1
name_housing_type	1
days_birth	1
days_employed	1
flag_mobil	1
flag_work_phone	1
flag_phone	1
flag_email	1
occupation_type	103,342
cnt_fam_members	1
target	0

There are 2 interesting things to notice:

The column occupation_type has lots of NA value. How much in proportion to the total? Should we drop the NAs or keep them?

df %>%
  summarize(
    freq_abs = sum(is.na(occupation_type)),
    freq_rel = sum(is.na(occupation_type)) / nrow(df)
    ) %>% 
  gt() %>% 
  tab_options(
    table.width = pct(100)
    ) %>% 
  cols_align(align = "center") %>% 
  cols_label(
    freq_abs = "Absolute Frequency",
    freq_rel = "Relative Frequency"
  ) %>% 
  tab_header(
    title = "Absolute and Relative Frequency",
    subtitle = "NA frequency for occupation_type"
  ) %>% 
  fmt_number(
    columns = c("freq_abs", "freq_rel"),
    decimals = 2,
    drop_trailing_zeros = TRUE
  ) 

Table 3: NA frequency for occupation_type

Absolute and Relative Frequency
NA frequency for occupation_type
Absolute Frequency	Relative Frequency
103,342	0.31

30.54% of occupation_type are NA. This could be a big concern: how to handle it? Drop it means losing lots of data, keeping it could lead to impute wrong data, which could be worse.

Lets check how the occupation_type labels frequency.

df %>% 
  summarise(
    n = n(),
    .by = occupation_type
  ) %>% 
  arrange(desc(n)) %>% 
  gt() %>% 
  tab_options(
    table.width = pct(100)
    ) %>% 
  cols_align(align = "center") %>% 
  cols_label(
    occupation_type = "Occupation Type",
    n = "Frequency"
  ) %>% 
  tab_header(
    title = "Occupation Type Label Frequency"
  ) %>% 
  fmt_number(
    columns = c("n"),
    decimals = 2,
    drop_trailing_zeros = TRUE
  ) 

Table 4: Occupation type label frequency

Occupation Type Label Frequency
Occupation Type	Frequency
NA	103,342
Laborers	60,146
Core staff	33,527
Sales staff	31,652
Managers	27,384
Drivers	20,020
High skill tech staff	13,399
Accountants	12,281
Medicine staff	10,438
Cooking staff	6,248
Security staff	6,218
Cleaning staff	4,594
Private service staff	2,787
Low-skill Laborers	1,714
Secretaries	1,577
Waiters/barmen staff	1,245
Realty agents	852
HR staff	567
IT staff	436

So, here is the situation: we could impute the NA respecting the proportion of the other labels in the dataset or we can drop the NA values. I want to drop the NA. Even if we are losing a third of the data, we are not making assumption about the rest. Also, we still have 235085, which are plenty enough.

df_occ_type <- df %>% 
  filter(!is.na(occupation_type)) %>% 
  summarise(
    n = n(),
    .by = occupation_type
  ) %>% 
  arrange(desc(n))

# to extend the palette to the number of labels
extended_palette <- colorRampPalette(RColorBrewer::brewer.pal(9, "Purples"))(df_occ_type %>% nrow())

g <- ggplot(df_occ_type) +
  geom_col(aes(x = n, y = reorder(occupation_type, n), fill = reorder(occupation_type, n))) +
  theme_minimal() +
  labs(
    x = "Count",
    y = "Labels",
    title = "Occupation Type Label Frequency"
  ) +
  scale_fill_manual(values = extended_palette)

ggplotly(g)

Figure 2: Barplot Occupation Type Label frequency

There is a NA value in at least all the columns: is it possible it is the same observation?

First of all, we can create a list of the columns name which have only 1 NA, using the df_na tibble.

one_cols <- df_na %>% 
  filter(value == 1) %>% 
  pull(columns)
one_cols

[1] "name_family_status" "name_housing_type"  "days_birth"        
[4] "days_employed"      "flag_mobil"         "flag_work_phone"   
[7] "flag_phone"         "flag_email"         "cnt_fam_members"   

Then, to check multiple OR condition, we can use if_any. In this case, we are checking if multiple columns are NA, with the previous filter on only one NA in each column.

library(reticulate)

df %>% 
  filter(
    if_any(all_of(one_cols), ~ . %>% is.na())
  ) %>% 
  gt()  %>% 
  tab_options(
    table.width = pct(100)
    )

id	code_gender	flag_own_car	flag_own_realty	cnt_children	amt_income_total	name_income_type	name_education_type	name_family_status	name_housing_type	days_birth	days_employed	flag_mobil	flag_work_phone	flag_phone	flag_email	occupation_type	cnt_fam_members	target
6392180	F	N	N	0	67500	Working	Secondary / se	NA	NA	NaN	NaN	NaN	NaN	NaN	NaN	NA	NaN	0

py$id_to_remove <- df %>% 
  filter(
    if_any(one_cols, ~ . %>% is.na())
  ) %>% 
  pull(id)

Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
ℹ Please use `all_of()` or `any_of()` instead.
  # Was:
  data %>% select(one_cols)

  # Now:
  data %>% select(all_of(one_cols))

See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.

Interesting features

Lets also look at some interesting variable: click on the different tabs to see the frequency table for each of them.

# features to check
cols <- c(
  "cnt_children",
  "name_education_type",
  "name_family_status",
  "name_housing_type",
  "cnt_fam_members",
  "flag_own_car",
  "code_gender"
  )
# 
count_results <- map(cols, ~ df %>% filter(!is.na(occupation_type)) %>% count(.data[[.x]]))
names(count_results) <- cols

Count children

count_results$cnt_children %>% 
  arrange(desc(n)) %>% 
  gt() %>% 
  tab_options(
    table.width = pct(100)
    ) %>% 
  cols_align(align = "center") %>% 
  cols_label(
    cnt_children = "# Children",
    n = "Count"
  ) %>% 
  tab_header(
    title = "Count of Number of Children",
  ) %>% 
  fmt_number(
    columns = c("n"),
    decimals = 2,
    drop_trailing_zeros = TRUE,
    #drop_trailing_dec_mark = FALSE
  ) 

Table 5: Count of Number of Children

Count of Number of Children
# Children	Count
0	148,909
1	56,238
2	26,196
3	3,319
4	300
5	109
9	4
12	4
14	3
7	2
19	1

Education type

count_results$name_education_type %>% 
  arrange(desc(n)) %>% 
  gt()  %>% 
  tab_options(
    table.width = pct(100)
    ) %>% 
  cols_align(align = "center") %>% 
  cols_label(
    name_education_type = "Education Type",
    n = "Count"
  ) %>% 
  tab_header(
    title = "Count by Education Type",
  ) %>% 
  fmt_number(
    columns = c("n"),
    decimals = 2,
    drop_trailing_zeros = TRUE,
    #drop_trailing_dec_mark = FALSE
  ) 

Table 6: Count by Education Type

Count by Education Type
Education Type	Count
Secondary / secondary special	157,822
Higher education	66,693
Incomplete higher	8,799
Lower secondary	1,609
Academic degree	162

Family status

count_results$name_family_status %>% 
  arrange(desc(n)) %>% 
  gt()  %>% 
  tab_options(
    table.width = pct(100)
    ) %>% 
  cols_align(align = "center") %>% 
  cols_label(
    name_family_status = "Family Status",
    n = "Count"
  ) %>% 
  tab_header(
    title = "Count by Family Status",
  ) %>% 
  fmt_number(
    columns = c("n"),
    decimals = 2,
    drop_trailing_zeros = TRUE,
    #drop_trailing_dec_mark = FALSE
  ) 

Table 7: Count by Family Status

Count by Family Status
Family Status	Count
Married	164,310
Single / not married	30,551
Civil marriage	20,968
Separated	14,108
Widow	5,148

Housing type

count_results$name_housing_type %>% 
  arrange(desc(n)) %>% 
  gt()  %>% 
  tab_options(
    table.width = pct(100)
    ) %>% 
  cols_align(align = "center") %>% 
  cols_label(
    name_housing_type = "House Type",
    n = "Count"
  ) %>% 
  tab_header(
    title = "Count by House Type",
  ) %>% 
  fmt_number(
    columns = c("n"),
    decimals = 2,
    drop_trailing_zeros = TRUE,
    #drop_trailing_dec_mark = FALSE
  ) 

Table 8: Count by House Type

Count by House Type
House Type	Count
House / apartment	208,980
With parents	11,938
Municipal apartment	7,411
Rented apartment	3,549
Office apartment	2,270
Co-op apartment	937

Count family members

count_results$cnt_fam_members %>% 
  arrange(desc(n)) %>% 
  gt()  %>% 
  tab_options(
    table.width = pct(100)
    ) %>% 
  cols_align(align = "center") %>% 
  cols_label(
    cnt_fam_members = "Number of Family Members",
    n = "Count"
  ) %>% 
  tab_header(
    title = "Count by Number of Family Members",
  ) %>% 
  fmt_number(
    columns = c("n"),
    decimals = 2,
    drop_trailing_zeros = TRUE,
    #drop_trailing_dec_mark = FALSE
  ) 

Table 9: Count by Number of Family Members

Count by Number of Family Members
Number of Family Members	Count
2	118,852
3	49,232
1	38,902
4	24,556
5	3,138
6	282
7	109
11	4
14	4
15	3
9	2
20	1

Own car

count_results$flag_own_car %>% 
  arrange(desc(n)) %>% 
  gt()  %>% 
  tab_options(
    table.width = pct(100)
    ) %>% 
  cols_align(align = "center") %>% 
  cols_label(
    flag_own_car = "Own Car",
    n = "Count"
  ) %>% 
  tab_header(
    title = "Count by Own a Car",
  ) %>% 
  fmt_number(
    columns = c("n"),
    decimals = 2,
    drop_trailing_zeros = TRUE,
    #drop_trailing_dec_mark = FALSE
  ) 

Table 10: Count by Own a Car

Count by Own a Car
Own Car	Count
N	138,203
Y	96,882

Gender

count_results$code_gender %>% 
  arrange(desc(n)) %>% 
  gt() %>% 
  tab_options(
    table.width = pct(100)
    ) %>% 
  cols_align(align = "center") %>% 
  cols_label(
    code_gender = "Gender",
    n = "Count"
  ) %>% 
  tab_header(
    title = "Count by Gender",
  ) %>% 
  fmt_number(
    columns = c("n"),
    decimals = 2,
    drop_trailing_zeros = TRUE,
    #drop_trailing_dec_mark = FALSE
  ) 

Table 11: Count by Gender

Count by Gender
Gender	Count
F	147,505
M	87,580

5 Data preparation

From the EDA, we know we can drop

• NA occupation_type.
• the id 6.39218^{6}.

df = df[df.id != id_to_remove]
df = df[~df.occupation_type.isna()]

It could be useful to have the birthday and employed date not as integer, but proper date. We will check after if they are really useful.

df["birthday"] = df.days_birth.map(lambda x : (date.today() + relativedelta(days=x)).strftime("%Y-%m-%d"))
df["employed"] = df["days_employed"].apply(lambda x: (date.today() + relativedelta(days=x)).strftime("%Y-%m-%d") if x < 0 else "0")

6 Split

To take into consideration the unbalance and have it also in our split, we can use the stratify method.

x = df[config.features]
y = df.target

xtrain, xtest, ytrain, ytest = train_test_split(
  x, 
  y, 
  test_size = .2,
  stratify = y
)

7 Models

7.1 DecisionTreeClassifier

To address the model explainability, we can choose a CART model. This selection has 2 interesting point for our task:

1. Explainability, as already mentioned. This will help us explain the reason of rejection.
2. Feature selection. At this link you can find a paper which shows how embedded method as the tree models can have the same performance of traditional feature selection method.

7.1.1 GridSearchCV

Even if the result of the model are relatively easy to understand, the hyperparameter tuning has far too many possibility. This is where GridSearchCV() comes handy.

param_grid = {
  "dtc__criterion" : ["gini", "entropy", "log_loss"],
  "dtc__splitter" : ["best", "random"],
  "dtc__max_depth" : [2, 5, 10],
  "dtc__max_features" : [len(config.features)]
}

scoring = {
    "accuracy": "accuracy",
    "f1": "f1",
    "roc_auc": "roc_auc"
}

7.1.2 Pipeline

One of the many nice sklearn features is the Pipeline(). It has lots of important benefits:

• Code redundancy and reproducibility : it is like a recipe you can tune and run without worrying about to put togheter each step in the correct order.
• Avoid data leakege: the preprocessing steps applies only to the training dataset.
• Hyperparameter tuning: the Pipeline() integrates with GridSearchCV() and RandomizedSearchCV().
• Modularity: it enables tuning the various steps, removing or adding new one.

First, initialize the various preprocessor and the CART model.

ohe = OneHotEncoder()
ohe_bi = OneHotEncoder(drop="if_binary")
ss = StandardScaler()
mms = MinMaxScaler()
dtc = DecisionTreeClassifier(class_weight="balanced")

Using the ColumnTransformer(), we can apply the different preprocessing to the specific columns. The preprocessing is divided between:

• Encoding the binary labels to 0-1 using OneHotEncoding(drop="if_binary)".
• Encoding the remaining labels using OneHotEncoding(). I choose this over other categorical variables encoding because it avoid imputing a hierarchy.
• Standardize the numerical variables using StandardScaler(). I also have instantiated the MinMaxScaler(), but I am not using it.

The classifier is a DecisionTreeClassifier(), which the GridSearchCV() will tune.

All of these steps are putted togheter with the Pipeline().

preprocessor = ColumnTransformer(
  transformers=[
    ("binary_encoder", ohe_bi, config.col_bi),
    ("categorical_encoder", ohe, config.col_ohe),
    ("scaler", ss, config.col_ss)
    ],
    remainder='passthrough'
    )

pipe_dtc = Pipeline(
  steps=[
    ("preprocessor", preprocessor),
    ("dtc", dtc)
    ]
    )

Important

For the binary labels I had to use the OneHotEncoding(drop="if_binary"), otherwise other preprocessor would not work with the ColumnTransformer().

7.1.3 Fit

grid_search_dtc = GridSearchCV(
    estimator=pipe_dtc,
    param_grid=param_grid,
    cv=7,
    scoring=scoring,
    refit="accuracy",  # This will refit the model using the accuracy metric
    n_jobs=-1 # use all the available CPU
    )

grid_search_dtc.fit(xtrain, ytrain)
# save the model as pkl file
with open(config.path + "grid_search_dtc.pkl", "wb") as file:
  pickle.dump(grid_search_dtc, file)

with open(config.path + "grid_search_dtc.pkl", "rb") as file:
    dtc_model = pickle.load(file)


cv_results = pd.DataFrame(dtc_model.cv_results_)
model = dtc_model.best_estimator_

py$cv_results %>% 
  arrange(across(contains("rank"))) %>% 
  select(params, mean_test_accuracy, rank_test_accuracy, mean_test_f1, rank_test_f1, mean_test_roc_auc, rank_test_roc_auc) %>% 
  gt() %>%
  tab_header(
    title = "Cross Validation Results",
    subtitle = "Main metrics"
  ) %>% 
  fmt_number(
   columns = c("mean_test_accuracy", "mean_test_f1", "mean_test_roc_auc"),
   decimals = 3,
   drop_trailing_zeros = TRUE,
   drop_trailing_dec_mark = FALSE
  ) %>% 
  cols_align(
    align = "center"
  ) %>% 
  cols_label(
    params = "criterion - max_depth - max_features - splitter",
    mean_test_accuracy = "Mean Test Accuracy",
    rank_test_accuracy = "Rank Test Accuracy",
    mean_test_f1 = "Mean Test F1",
    rank_test_f1 = "Rank Test F1",
    mean_test_roc_auc = "Mean Test ROC AUC",
    rank_test_roc_auc = "Rank Test ROC AUC",
  ) %>% 
  tab_options(
    table.width = pct(100)
    )

Table 12: Cross Validation Results

Cross Validation Results
Main metrics
criterion - max_depth - max_features - splitter	Mean Test Accuracy	Rank Test Accuracy	Mean Test F1	Rank Test F1	Mean Test ROC AUC	Rank Test ROC AUC
log_loss, 2, 15, random	0.727	1	0.2	18	0.56	17
gini, 10, 15, best	0.707	2	0.403	1	0.863	2
log_loss, 10, 15, best	0.704	3	0.4	2	0.863	1
entropy, 10, 15, best	0.701	4	0.394	3	0.853	3
gini, 2, 15, best	0.677	5	0.31	8	0.705	11
entropy, 5, 15, best	0.667	6	0.368	4	0.82	4
log_loss, 5, 15, best	0.656	7	0.365	5	0.819	5
gini, 5, 15, best	0.651	8	0.348	6	0.797	6
log_loss, 10, 15, random	0.605	9	0.296	9	0.741	7
entropy, 10, 15, random	0.602	10	0.274	12	0.706	10
log_loss, 2, 15, best	0.591	11	0.31	7	0.715	9
gini, 10, 15, random	0.583	12	0.277	11	0.722	8
entropy, 5, 15, random	0.56	13	0.231	15	0.622	15
entropy, 2, 15, best	0.543	14	0.285	10	0.677	12
gini, 5, 15, random	0.541	15	0.234	14	0.633	14
log_loss, 5, 15, random	0.515	16	0.246	13	0.65	13
gini, 2, 15, random	0.513	17	0.207	16	0.567	16
entropy, 2, 15, random	0.341	18	0.203	17	0.549	18

The grid search returns a CART with a best score of best_score with the following parameters:

• criterion:
• max_depth:
• splitter:

Now, we can save and load the model. This is useful to avoid to run the grid_search.

7.1.4 Predict

ypred = model.predict(xtest)

7.1.5 Classification Report

cr = classification_report(
  ytest,
  ypred,
  # target_names=config.labels,
  digits=4,
  output_dict=True
  )
df_cr = pd.DataFrame.from_dict(cr).reset_index()

library(reticulate)
df_cr <- py$df_cr %>% dplyr::rename(names = index)
cols <- df_cr %>% colnames()
df_cr %>% 
  pivot_longer(
    cols = -names,
    names_to = "metrics",
    values_to = "values"
  ) %>% 
  pivot_wider(
    names_from = names,
    values_from = values
  ) %>% 
  gt() %>%
  tab_header(
    title = "Confusion Matrix",
    subtitle = "Threshold optimization favoring recall"
  ) %>% 
  fmt_number(
    columns = c("precision", "recall", "f1-score", "support"),
    decimals = 2,
    drop_trailing_zeros = TRUE,
    drop_trailing_dec_mark = FALSE
  ) %>% 
  cols_align(
    align = "center",
    columns = c("precision", "recall", "f1-score", "support")
  ) %>% 
  cols_align(
    align = "left",
    columns = metrics
  ) %>% 
  cols_label(
    metrics = "Metrics",
    precision = "Precision",
    recall = "Recall",
    `f1-score` = "F1-Score",
    support = "Support"
  ) %>% 
  tab_options(
    table.width = pct(100)
    )

Table 13: Classification report

Confusion Matrix
Threshold optimization favoring recall
Metrics	Precision	Recall	F1-Score	Support
0	0.94	0.48	0.63	41,986.
1	0.15	0.76	0.25	5,031.
accuracy	0.51	0.51	0.51	0.51
macro avg	0.55	0.62	0.44	47,017.
weighted avg	0.86	0.51	0.59	47,017.

CLASSIFICATION ANALYSIS

7.2 Random Forest Classifier

7.3 XGBoost Classifier